Endowing protein language models with structural knowledge.

Area of Science:

• Computational Biology
• Structural Bioinformatics
• Machine Learning for Proteins

Background:

• Protein language models (PLMs) excel at learning from sequence data but often overlook structural information.
• Integrating structural data into PLMs is computationally intensive and complex.
• Existing methods limit practical adoption due to high resource requirements.

Purpose of the Study:

• To develop a computationally efficient joint sequence and structure embedding method for proteins.
• To enable seamless incorporation of structural knowledge into existing PLMs.
• To balance high performance with practical computational constraints.

Main Methods:

• A novel lightweight integration framework combining pretrained sequence transformers with specialized structural adapters.
• Enhanced self-attention mechanisms to incorporate structural knowledge.
• Modest pretraining on 542K protein structures using masked language modeling.

Main Results:

• Achieved computational and parameter efficiency.
• Outperformed sequence-only models like ESM-2.
• Matched performance of complex structure-based methods with significantly fewer resources.
• Demonstrated efficiency with modest pretraining data.

Conclusions:

• Established a new paradigm for protein representation learning.
• Provided an accessible tool for capturing both sequence and structural protein information.
• Overcame the computational overhead typically associated with structure-aware models.